Very low-resource languages, having only a few million tokens worth of data,
are not well-supported by multilingual NLP approaches due to poor quality
cross-lingual word representations. Recent work showed that good cross-lingual
performance can be achieved if a source language is related to the low-resource
target language. However, not all language pairs are related. In this paper, we
propose to build multilingual word embeddings (MWEs) via a novel language
chain-based approach, that incorporates intermediate related languages to
bridge the gap between the distant source and target. We build MWEs one
language at a time by starting from the resource rich source and sequentially
adding each language in the chain till we reach the target. We extend a
semi-joint bilingual approach to multiple languages in order to eliminate the
main weakness of previous works, i.e., independently trained monolingual
embeddings, by anchoring the target language around the multilingual space. We
evaluate our method on bilingual lexicon induction for 4 language families,
involving 4 very low-resource (<5M tokens) and 4 moderately low-resource (<50M)
target languages, showing improved performance in both categories.
Additionally, our analysis reveals the importance of good quality embeddings
for intermediate languages as well as the importance of leveraging anchor
points from all languages in the multilingual space.

通过一种基于语言链的新方法构建多语言词嵌入（MWEs），该方法通过中间相关语言弥合远源和目标的差距。我们通过在语言链中逐个添加每种语言，从资源丰富的源语言开始，构建 MWEs。我们将半联合双语方法扩展到多种语言，以消除以前工作的主要弱点，即独立训练的单语词嵌入，并使目标语言围绕多语言空间进行锚定。我们在涉及 4 个非常低资源（<5M 令牌）和 4 个适度低资源（<50M）目标语言的 4 个语言家族的双语词典感应中评估了我们的方法，显示了两个类别的改进性能。此外，我们的分析揭示了中间语言的高质量嵌入的重要性，以及利用多语言空间中所有语言的锚点的重要性。